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import omicverse as ov
import scanpy as sc
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
ov.utils.ov_plot_set()
import omicverse as ov
import scanpy as sc
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
ov.utils.ov_plot_set()
/Users/julia/miniforge3/envs/nocd/lib/python3.8/site-packages/phate/__init__.py
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sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')
scanpy==1.9.1 anndata==0.8.0 umap==0.5.3 numpy==1.22.4 scipy==1.9.1 pandas==1.5.3 scikit-learn==0.24.0 statsmodels==0.13.2 python-igraph==0.10.4 louvain==0.8.0 pynndescent==0.5.7
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adata = sc.read_10x_mtx(
'Pyomic/CRCdata/', # the directory with the `.mtx` file
var_names='gene_symbols', # use gene symbols for the variable names (variables-axis index)
cache=True)
adata = sc.read_10x_mtx(
'Pyomic/CRCdata/', # the directory with the `.mtx` file
var_names='gene_symbols', # use gene symbols for the variable names (variables-axis index)
cache=True)
... reading from cache file cache/Users-julia-USTB-Pyomic-CRCdata-matrix.h5ad
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adata
adata
Out[12]:
AnnData object with n_obs × n_vars = 140281 × 33694
var: 'gene_ids'
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adata.var_names_make_unique()
adata.var_names_make_unique()
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adata=ov.single.scanpy_lazy(adata)
adata
adata=ov.single.scanpy_lazy(adata)
adata
filtered out 1 cells that have less than 200 genes expressed
Running Scrublet
normalizing counts per cell
finished (0:00:00)
extracting highly variable genes
finished (0:00:02)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
normalizing counts per cell
finished (0:00:00)
normalizing counts per cell
finished (0:00:00)
Embedding transcriptomes using PCA...
Automatically set threshold at doublet score = 0.81
Detected doublet rate = 0.0%
Estimated detectable doublet fraction = 0.3%
Overall doublet rate:
Expected = 5.0%
Estimated = 0.3%
Scrublet finished (0:04:46)
normalizing counts per cell
finished (0:00:00)
extracting highly variable genes
finished (0:00:01)
--> added
'highly_variable', boolean vector (adata.var)
'means', float vector (adata.var)
'dispersions', float vector (adata.var)
'dispersions_norm', float vector (adata.var)
... as `zero_center=True`, sparse input is densified and may lead to large memory consumption
computing PCA
Note that scikit-learn's randomized PCA might not be exactly reproducible across different computational platforms. For exact reproducibility, choose `svd_solver='arpack'.`
on highly variable genes
with n_comps=100
finished (0:00:14)
computing neighbors
using 'X_pca' with n_pcs = 50
OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
finished: added to `.uns['neighbors']`
`.obsp['distances']`, distances for each pair of neighbors
`.obsp['connectivities']`, weighted adjacency matrix (0:00:16)
running Leiden clustering
finished: found 28 clusters and added
'leiden', the cluster labels (adata.obs, categorical) (0:00:32)
running PAGA
finished: added
'paga/connectivities', connectivities adjacency (adata.uns)
'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:03)
--> added 'pos', the PAGA positions (adata.uns['paga'])
computing UMAP
finished: added
'X_umap', UMAP coordinates (adata.obsm) (0:00:52)
Out[6]:
AnnData object with n_obs × n_vars = 132726 × 1458
obs: 'n_genes', 'doublet_score', 'predicted_doublet', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'leiden'
var: 'gene_ids', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'leiden', 'paga', 'leiden_sizes', 'umap'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
layers: 'counts'
obsp: 'distances', 'connectivities'
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scsa=ov.single.pySCSA(adata=adata,
foldchange=1.5,
pvalue=0.01,
celltype='cancer',
target='cellmarker',
tissue='All',
)
scsa=ov.single.pySCSA(adata=adata,
foldchange=1.5,
pvalue=0.01,
celltype='cancer',
target='cellmarker',
tissue='All',
)
......Loading dataset from temp/pySCSA_2023.db
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scsa.cell_anno(clustertype='leiden',
cluster='all')
scsa.cell_anno(clustertype='leiden',
cluster='all')
...Auto annotate cell Version V2.0 [2023/04/07] DB load: GO_items:47347,Human_GO:3,Mouse_GO:3, CellMarkers:96074,CancerSEA:1574, Ensembl_HGNC:61541,Ensembl_Mouse:55414 Namespace(Gensymbol=True, MarkerDB=None, celltype='cancer', cluster='all', db='temp/pySCSA_2023.db', fc='/Users/julia/Library/Jupyter/runtime/kernel-a3c7a1b2-05ce-42af-8409-32434bc4e30b.json', foldchange=1.5, input='temp/rna.csv', list_tissue=False, noprint=True, norefdb=False, outfmt='txt', output='temp/rna_anno.txt', pvalue=0.01, source='scanpy', species='Human', target='cellmarker', tissue='All', weight=100.0) Version V2.0 [2023/04/07] DB load: GO_items:47347,Human_GO:3,Mouse_GO:3, CellMarkers:96074,CancerSEA:1574, Ensembl_HGNC:61541,Ensembl_Mouse:55414 load markers: 15107 Cluster 0 Gene number: 1560 Other Gene number: 1763 Cluster 1 Gene number: 17 Other Gene number: 1795 Cluster 10 Gene number: 332 Other Gene number: 1779 Cluster 11 Gene number: 2707 Other Gene number: 1775 Cluster 12 Gene number: 359 Other Gene number: 1759 Cluster 13 Gene number: 2268 Other Gene number: 1778 Cluster 14 Gene number: 79 Other Gene number: 1794 Cluster 15 Gene number: 951 Other Gene number: 1734 Cluster 16 Gene number: 135 Other Gene number: 1788 Cluster 17 Gene number: 945 Other Gene number: 1788 Cluster 18 Gene number: 735 Other Gene number: 1786 Cluster 19 Gene number: 119 Other Gene number: 1794 Cluster 2 Gene number: 184 Other Gene number: 1778 Cluster 20 Gene number: 2314 Other Gene number: 1779 Cluster 21 Gene number: 234 Other Gene number: 1792 Cluster 22 Gene number: 2020 Other Gene number: 1707 Cluster 23 Gene number: 1863 Other Gene number: 1785 Cluster 24 Gene number: 254 Other Gene number: 1796 Cluster 25 Gene number: 363 Other Gene number: 1775 Cluster 26 Gene number: 694 Other Gene number: 1762 Cluster 27 Gene number: 1020 Other Gene number: 1762 Cluster 3 Gene number: 76 Other Gene number: 1796 Cluster 4 Gene number: 22 Other Gene number: 1796 Cluster 5 Gene number: 281 Other Gene number: 1778 Cluster 6 Gene number: 194 Other Gene number: 1787 Cluster 7 Gene number: 61 Other Gene number: 1795 Cluster 8 Gene number: 236 Other Gene number: 1763 Cluster 9 Gene number: 18 Other Gene number: 1797 #Cluster Type Celltype Score Times ['0', 'Good', 'Macrophage', 10.98725966386757, 2.0280529038442787] ['1', '?', 'CD4+ T cell|T cell', '2.9235818135699008|2.8118674528982326', 1.0397295969824334] ['10', '?', 'T cell|Exhausted CD8+ T cell', '7.790140548602346|4.6921252337281185', 1.660258445918057] ['11', '?', 'Epithelial cell|Cancer cell', '7.748296882852158|7.591000531107029', 1.0207214254696133] ['12', '?', 'Plasma cell|B cell', '5.999929491023616|5.1608544569114345', 1.1625845179548688] ['13', '?', 'Cancer stem cell|Epithelial cell', '8.686237370283646|6.948647143229323', 1.2500616582247106] ['14', '?', 'Natural killer cell|T cell', '5.501651577120244|4.992267862896303', 1.102034531842692] ['15', '?', 'T cell|CD8+ T cell', '7.55653116853752|5.711383347363439', 1.3230649579888314] ['16', '?', 'T cell|Exhausted CD8+ T cell', '6.315611039528545|4.228965684733908', 1.4934174241061338] ['17', '?', 'Dendritic cell|Macrophage', '7.4358939124216255|6.587746560774603', 1.1287462023352786] ['18', '?', 'Macrophage|Myeloid cell', '6.9007403491894745|6.841019068877939', 1.008729880696171] ['19', 'Good', 'Natural killer cell', 8.903331230342022, 3.347749377065685] ['2', '?', 'Naive T(Th0) cell|Naive CD8+ T cell', '6.1739621467516645|5.13154749620691', 1.2031384589766103] ['20', '?', 'Cancer stem cell|Epithelial cell', '9.486282737404133|5.998357402590407', 1.5814800787474679] ['21', '?', 'T cell|Regulatory T(Treg) cell', '6.0611270692482755|5.80248097358032', 1.044575087250715] ['22', 'Good', 'Fibroblast', 10.934684646134345, 2.350176686721174] ['23', '?', 'Epithelial cell|Cancer cell', '8.804846466370549|6.379032981870284', 1.3802791883024619] ['24', 'Good', 'Macrophage', 9.07277897765793, 2.221130940240644] ['25', 'Good', 'Mast cell', 10.013955925648537, 2.5912124431946575] ['26', '?', 'Plasmacytoid dendritic cell(pDC)|B cell', '6.8972999672294515|6.280934230208629', 1.0981328118445126] ['27', 'Good', 'Endothelial cell', 11.373854852300308, 2.8161484708273803] ['3', '?', 'T cell|CD8+ T cell', '3.7083772452565515|3.4171438552747775', 1.0852271377256242] ['4', '?', 'T cell|CD8+ T cell', '5.273602983409775|4.461891753317525', 1.1819208701082278] ['5', 'Good', 'Natural killer cell', 10.018815594460941, 5.096089671718397] ['6', '?', 'T cell|Regulatory T(Treg) cell', '8.197942381866364|6.804275159154912', 1.2048222904149197] ['7', '?', 'Natural killer cell|T cell', '6.36097141026852|5.674557054641735', 1.1209635129257014] ['8', 'Good', 'B cell', 9.163232987072572, 8.294660710025221] ['9', '?', 'B cell|Plasma cell', '5.404267493386934|4.526320502096333', 1.1939648310109694]
Out[5]:
| Cell Type | Z-score | Cluster | |
|---|---|---|---|
| 0 | Macrophage | 10.987260 | 0 |
| 1 | Myeloid cell | 5.417640 | 0 |
| 2 | Monocyte | 3.627153 | 0 |
| 3 | M2 macrophage | 3.206632 | 0 |
| 4 | Cancer stem cell | 2.608150 | 0 |
| ... | ... | ... | ... |
| 3515 | T helper(Th) cell | -0.266296 | 9 |
| 3516 | T helper1 (Th1) cell | -0.266296 | 9 |
| 3517 | T helper17 (Th17) cell | -0.266296 | 9 |
| 3518 | T helper2 (Th2) cell | -0.266296 | 9 |
| 3519 | Conventional dendritic cell 2a(cDC2a) | -0.266296 | 9 |
3520 rows × 3 columns
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scsa.cell_anno
scsa.cell_anno
Out[6]:
<bound method pySCSA.cell_anno of <Pyomic.single._anno.pySCSA object at 0x2a25c50a0>>
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scsa.cell_auto_anno(adata)
scsa.cell_auto_anno(adata)
...cell type added to scsa_celltype on obs of anndata
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sc.pl.umap(adata, color=['leiden','scsa_celltype'],
wspace=0.5,
frameon=False,
palette=ov.utils.pyomic_palette(),
legend_loc='on data',
legend_fontsize=10,
)
sc.pl.umap(adata, color=['leiden','scsa_celltype'],
wspace=0.5,
frameon=False,
palette=ov.utils.pyomic_palette(),
legend_loc='on data',
legend_fontsize=10,
)
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res_marker_dict={
'T cell': ['CD3D', 'CD3G', 'TRAC'],
'B cell': ['CD79A', 'CD19', 'MS4A1'],
'Plasma cell': ['IGHG1', 'IGHA1', 'MZB1', 'CD79A'],
'Myeloid cell': ['CD68', 'CD163', 'LYZ', 'CD14'],
'Natural killer cell': ['KLRF1', 'KLRD1', 'FGFBP2', 'PRF1'],
'Fibroblast': ['FAP', 'COL1A1', 'COL3A1', 'DCN', 'ACTA2'],
'Endothelial cell': ['CLDN5', 'CDH5'],
'Plasmacytoid dendritic cell(pDC)': ['LILRA4', 'IL3RA'],
'Epithelial cell': ['EPCAM'],
'Mast cell': ['TPSAB1', 'TPSB2', 'MS4A2'],
}
res_marker_dict={
'T cell': ['CD3D', 'CD3G', 'TRAC'],
'B cell': ['CD79A', 'CD19', 'MS4A1'],
'Plasma cell': ['IGHG1', 'IGHA1', 'MZB1', 'CD79A'],
'Myeloid cell': ['CD68', 'CD163', 'LYZ', 'CD14'],
'Natural killer cell': ['KLRF1', 'KLRD1', 'FGFBP2', 'PRF1'],
'Fibroblast': ['FAP', 'COL1A1', 'COL3A1', 'DCN', 'ACTA2'],
'Endothelial cell': ['CLDN5', 'CDH5'],
'Plasmacytoid dendritic cell(pDC)': ['LILRA4', 'IL3RA'],
'Epithelial cell': ['EPCAM'],
'Mast cell': ['TPSAB1', 'TPSB2', 'MS4A2'],
}
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sc.tl.dendrogram(adata,'leiden')
sc.pl.dotplot(adata, res_marker_dict, 'leiden', dendrogram=True, standard_scale='var')
sc.tl.dendrogram(adata,'leiden')
sc.pl.dotplot(adata, res_marker_dict, 'leiden', dendrogram=True, standard_scale='var')
using 'X_pca' with n_pcs = 100 Storing dendrogram info using `.uns['dendrogram_leiden']` WARNING: Groups are not reordered because the `groupby` categories and the `var_group_labels` are different. categories: 0, 1, 2, etc. var_group_labels: T cell, B cell, Plasma cell, etc.
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cluster2annotation = {
'0': 'Myeloid cell',
'1': 'T cell',
'2': 'T cell',
'3': 'T cell',
'4': 'T cell',
'5': 'Natural killer cell',
'6': 'T cell',
'7': 'T cell',
'8': 'B cell',
'9': 'T cell',
'10': 'T cell',
'11': 'Epithelial cell',
'12': 'Plasma cell',
'13': 'Epithelial cell',
'14': 'T cell',
'15': 'T cell',
'16': 'T cell',
'17': 'Myeloid cell',
'18': 'Myeloid cell',
'19': 'Natural killer cell',
'20': 'Epithelial cell',
'21': 'T cell',
'22': 'Fibroblast',
'23': 'Epithelial cell',
'24': 'Myeloid cell',
'25': 'Mast cell',
'26': 'Plasmacytoid dendritic cell(pDC)',
'27': 'Endothelial cell',
}
cluster2annotation = {
'0': 'Myeloid cell',
'1': 'T cell',
'2': 'T cell',
'3': 'T cell',
'4': 'T cell',
'5': 'Natural killer cell',
'6': 'T cell',
'7': 'T cell',
'8': 'B cell',
'9': 'T cell',
'10': 'T cell',
'11': 'Epithelial cell',
'12': 'Plasma cell',
'13': 'Epithelial cell',
'14': 'T cell',
'15': 'T cell',
'16': 'T cell',
'17': 'Myeloid cell',
'18': 'Myeloid cell',
'19': 'Natural killer cell',
'20': 'Epithelial cell',
'21': 'T cell',
'22': 'Fibroblast',
'23': 'Epithelial cell',
'24': 'Myeloid cell',
'25': 'Mast cell',
'26': 'Plasmacytoid dendritic cell(pDC)',
'27': 'Endothelial cell',
}
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ov.single.scanpy_cellanno_from_dict(adata,anno_dict=cluster2annotation,
clustertype='leiden')
ov.single.scanpy_cellanno_from_dict(adata,anno_dict=cluster2annotation,
clustertype='leiden')
...cell type added to major_celltype on obs of anndata
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sc.pl.umap(adata, color=['major_celltype','scsa_celltype'], wspace=0.5,
frameon=False,
palette=ov.utils.pyomic_palette(),
legend_loc='on data',
legend_fontsize=10)
sc.pl.umap(adata, color=['major_celltype','scsa_celltype'], wspace=0.5,
frameon=False,
palette=ov.utils.pyomic_palette(),
legend_loc='on data',
legend_fontsize=10)
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adata.write(results_file)
adata.write(results_file)
Evaluation¶
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import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
sns.set()
import seaborn as sns
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
sns.set()
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adata.obs.major_celltype.cat.categories
adata.obs.major_celltype.cat.categories
Out[69]:
Index(['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Mast cell', 'Myeloid cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'],
dtype='object')
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adata.obs.scsa_celltype.cat.categories
adata.obs.scsa_celltype.cat.categories
Out[70]:
Index(['B cell', 'CD4+ T cell', 'Cancer stem cell', 'Dendritic cell',
'Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Macrophage',
'Mast cell', 'Naive T(Th0) cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'],
dtype='object')
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mat=np.zeros((14,10))
mat=np.zeros((14,10))
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mat
mat
Out[43]:
array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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pd_mat=pd.DataFrame(mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Macrophage',
'Mast cell', 'Naive T(Th0) cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'CD4+ T cell', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Mast cell', 'Myeloid cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'])
pd_mat=pd.DataFrame(mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Macrophage',
'Mast cell', 'Naive T(Th0) cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'CD4+ T cell', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Mast cell', 'Myeloid cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'])
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for i in range(len(adata.obs)):
scsa_celltype = adata.obs.iloc[i]['scsa_celltype']
major_celltype = adata.obs.iloc[i]['major_celltype']
pd_mat.loc[scsa_celltype, major_celltype] += 1
pd_mat
for i in range(len(adata.obs)):
scsa_celltype = adata.obs.iloc[i]['scsa_celltype']
major_celltype = adata.obs.iloc[i]['major_celltype']
pd_mat.loc[scsa_celltype, major_celltype] += 1
pd_mat
Out[13]:
| B cell | Endothelial cell | Epithelial cell | Fibroblast | Myeloid cell | Mast cell | Natural killer cell | Plasma cell | Plasmacytoid dendritic cell(pDC) | T cell | |
|---|---|---|---|---|---|---|---|---|---|---|
| B cell | 7006.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4769.0 |
| Cancer stem cell | 0.0 | 0.0 | 5563.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Dendritic cell | 0.0 | 0.0 | 0.0 | 0.0 | 2512.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Endothelial cell | 0.0 | 307.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Epithelial cell | 0.0 | 0.0 | 5847.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Fibroblast | 0.0 | 0.0 | 0.0 | 1600.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Myeloid cell | 0.0 | 0.0 | 0.0 | 0.0 | 15355.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Mast cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 771.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Natural killer cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 11410.0 | 0.0 | 0.0 | 10790.0 |
| Plasma cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4090.0 | 0.0 | 0.0 |
| Plasmacytoid dendritic cell(pDC) | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 557.0 | 0.0 |
| T cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 62149.0 |
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from sklearn.preprocessing import MinMaxScaler
import numpy as np
scaler = MinMaxScaler()
from sklearn.preprocessing import MinMaxScaler
import numpy as np
scaler = MinMaxScaler()
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normalized_mat = scaler.fit_transform(mat)
normalized_mat = scaler.fit_transform(mat)
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normalized_mat
normalized_mat
Out[16]:
array([[1. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.07673494],
[0. , 0. , 0.95142808, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0.16359492,
0. , 0. , 0. , 0. , 0. ],
[0. , 1. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 1. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 1. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
1. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
0. , 1. , 0. , 0. , 0.17361502],
[0. , 0. , 0. , 0. , 0. ,
0. , 0. , 1. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. ]])
In [17]:
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pd_nor_mat=pd.DataFrame(normalized_mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Macrophage',
'Mast cell', 'Naive T(Th0) cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'CD4+ T cell', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Mast cell', 'Myeloid cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'])
pd_nor_mat=pd.DataFrame(normalized_mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Macrophage',
'Mast cell', 'Naive T(Th0) cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'CD4+ T cell', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Mast cell', 'Myeloid cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'])
In [18]:
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pd_nor_mat
pd_nor_mat
Out[18]:
| B cell | Endothelial cell | Epithelial cell | Fibroblast | Myeloid cell | Mast cell | Natural killer cell | Plasma cell | Plasmacytoid dendritic cell(pDC) | T cell | |
|---|---|---|---|---|---|---|---|---|---|---|
| B cell | 1.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.076735 |
| Cancer stem cell | 0.0 | 0.0 | 0.951428 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Dendritic cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.163595 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Endothelial cell | 0.0 | 1.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Epithelial cell | 0.0 | 0.0 | 1.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Fibroblast | 0.0 | 0.0 | 0.000000 | 1.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Myeloid cell | 0.0 | 0.0 | 0.000000 | 0.0 | 1.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Mast cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 1.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Natural killer cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 1.0 | 0.0 | 0.0 | 0.173615 |
| Plasma cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 1.0 | 0.0 | 0.000000 |
| Plasmacytoid dendritic cell(pDC) | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 1.0 | 0.000000 |
| T cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 1.000000 |
In [103]:
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from sklearn.metrics import adjusted_mutual_info_score
from sklearn.metrics import f1_score
f1 = f1_score(adata.obs['major_celltype'],adata.obs['scsa_true_celltype'], average='weighted')
ami_score = adjusted_mutual_info_score(adata.obs['major_celltype'],adata.obs['scsa_true_celltype'])
print("F1 score with average='weighted':", f1)
print("AMI score':", ami_score)
from sklearn.metrics import adjusted_mutual_info_score
from sklearn.metrics import f1_score
f1 = f1_score(adata.obs['major_celltype'],adata.obs['scsa_true_celltype'], average='weighted')
ami_score = adjusted_mutual_info_score(adata.obs['major_celltype'],adata.obs['scsa_true_celltype'])
print("F1 score with average='weighted':", f1)
print("AMI score':", ami_score)
F1 score with average='weighted': 0.8559731414725444 AMI score': 0.7871453798045023
In [109]:
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fig,ax = plt.subplots(figsize=(10, 8))
sns.heatmap(pd_nor_mat, cmap="Blues")
plt.ylabel('scsa_celltype', fontsize=8)
plt.xlabel('major_celltype', fontsize=8)
plt.xticks(fontsize=6)
plt.yticks(fontsize=6)
plt.savefig('confusion.pdf', bbox_inches='tight')
fig,ax = plt.subplots(figsize=(10, 8))
sns.heatmap(pd_nor_mat, cmap="Blues")
plt.ylabel('scsa_celltype', fontsize=8)
plt.xlabel('major_celltype', fontsize=8)
plt.xticks(fontsize=6)
plt.yticks(fontsize=6)
plt.savefig('confusion.pdf', bbox_inches='tight')
Merging cells of the same type¶
In [56]:
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cluster_scsa = {
'B cell':'B cell',
'Cancer stem cell':'Cancer stem cell',
'Dendritic cell':'Dendritic cell',
'Endothelial cell':'Endothelial cell',
'Epithelial cell':'Epithelial cell',
'Fibroblast':'Fibroblast',
'Macrophage':'Myeloid cell',
'Mast cell': 'Mast cell',
'Naive T(Th0) cell': 'T cell',
'Natural killer cell':'Natural killer cell',
'Plasma cell':'Plasma cell',
'Plasmacytoid dendritic cell(pDC)':'Plasmacytoid dendritic cell(pDC)',
'CD4+ T cell':'T cell',
'T cell':'T cell',
}
cluster_scsa = {
'B cell':'B cell',
'Cancer stem cell':'Cancer stem cell',
'Dendritic cell':'Dendritic cell',
'Endothelial cell':'Endothelial cell',
'Epithelial cell':'Epithelial cell',
'Fibroblast':'Fibroblast',
'Macrophage':'Myeloid cell',
'Mast cell': 'Mast cell',
'Naive T(Th0) cell': 'T cell',
'Natural killer cell':'Natural killer cell',
'Plasma cell':'Plasma cell',
'Plasmacytoid dendritic cell(pDC)':'Plasmacytoid dendritic cell(pDC)',
'CD4+ T cell':'T cell',
'T cell':'T cell',
}
In [57]:
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adata.obs['scsa_true_celltype'] = adata.obs['scsa_celltype'].map(cluster_scsa).astype('category')
adata.obs['scsa_true_celltype'] = adata.obs['scsa_celltype'].map(cluster_scsa).astype('category')
In [6]:
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mat=np.zeros((12,10))
mat
mat=np.zeros((12,10))
mat
Out[6]:
array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
In [7]:
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pd_mat=pd.DataFrame(mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Myeloid cell',
'Mast cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Myeloid cell', 'Mast cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'])
pd_mat=pd.DataFrame(mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Myeloid cell',
'Mast cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Myeloid cell', 'Mast cell', 'Natural killer cell', 'Plasma cell',
'Plasmacytoid dendritic cell(pDC)', 'T cell'])
In [8]:
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for i in range(len(adata.obs)):
scsa_celltype = adata.obs.iloc[i]['scsa_true_celltype']
major_celltype = adata.obs.iloc[i]['major_celltype']
pd_mat.loc[scsa_celltype, major_celltype] += 1
pd_mat
for i in range(len(adata.obs)):
scsa_celltype = adata.obs.iloc[i]['scsa_true_celltype']
major_celltype = adata.obs.iloc[i]['major_celltype']
pd_mat.loc[scsa_celltype, major_celltype] += 1
pd_mat
Out[8]:
| B cell | Endothelial cell | Epithelial cell | Fibroblast | Myeloid cell | Mast cell | Natural killer cell | Plasma cell | Plasmacytoid dendritic cell(pDC) | T cell | |
|---|---|---|---|---|---|---|---|---|---|---|
| B cell | 7006.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4769.0 |
| Cancer stem cell | 0.0 | 0.0 | 5563.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Dendritic cell | 0.0 | 0.0 | 0.0 | 0.0 | 2512.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Endothelial cell | 0.0 | 307.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Epithelial cell | 0.0 | 0.0 | 5847.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Fibroblast | 0.0 | 0.0 | 0.0 | 1600.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Myeloid cell | 0.0 | 0.0 | 0.0 | 0.0 | 15355.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Mast cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 771.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Natural killer cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 11410.0 | 0.0 | 0.0 | 10790.0 |
| Plasma cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4090.0 | 0.0 | 0.0 |
| Plasmacytoid dendritic cell(pDC) | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 557.0 | 0.0 |
| T cell | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 62149.0 |
In [9]:
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from sklearn.preprocessing import MinMaxScaler
import numpy as np
scaler = MinMaxScaler()
from sklearn.preprocessing import MinMaxScaler
import numpy as np
scaler = MinMaxScaler()
In [10]:
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normalized_mat = scaler.fit_transform(mat)
normalized_mat
normalized_mat = scaler.fit_transform(mat)
normalized_mat
Out[10]:
array([[1. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.07673494],
[0. , 0. , 0.95142808, 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0.16359492,
0. , 0. , 0. , 0. , 0. ],
[0. , 1. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 1. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 1. , 0. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 1. ,
0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
1. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
0. , 1. , 0. , 0. , 0.17361502],
[0. , 0. , 0. , 0. , 0. ,
0. , 0. , 1. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. ],
[0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. ]])
In [16]:
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pd_nor_mat=pd.DataFrame(normalized_mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Myeloid cell',
'Mast cell', 'Natural killer cell', 'Plasma cell',
'pDC', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Myeloid cell', 'Mast cell', 'Natural killer cell', 'Plasma cell',
'pDC', 'T cell'])
pd_nor_mat=pd.DataFrame(normalized_mat, index=['B cell', 'Cancer stem cell', 'Dendritic cell','Endothelial cell', 'Epithelial cell', 'Fibroblast', 'Myeloid cell',
'Mast cell', 'Natural killer cell', 'Plasma cell',
'pDC', 'T cell'], columns=['B cell', 'Endothelial cell', 'Epithelial cell', 'Fibroblast',
'Myeloid cell', 'Mast cell', 'Natural killer cell', 'Plasma cell',
'pDC', 'T cell'])
In [17]:
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pd_nor_mat
pd_nor_mat
Out[17]:
| B cell | Endothelial cell | Epithelial cell | Fibroblast | Myeloid cell | Mast cell | Natural killer cell | Plasma cell | pDC | T cell | |
|---|---|---|---|---|---|---|---|---|---|---|
| B cell | 1.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.076735 |
| Cancer stem cell | 0.0 | 0.0 | 0.951428 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Dendritic cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.163595 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Endothelial cell | 0.0 | 1.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Epithelial cell | 0.0 | 0.0 | 1.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Fibroblast | 0.0 | 0.0 | 0.000000 | 1.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Myeloid cell | 0.0 | 0.0 | 0.000000 | 0.0 | 1.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Mast cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 1.0 | 0.0 | 0.0 | 0.0 | 0.000000 |
| Natural killer cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 1.0 | 0.0 | 0.0 | 0.173615 |
| Plasma cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 1.0 | 0.0 | 0.000000 |
| pDC | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 1.0 | 0.000000 |
| T cell | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 1.000000 |
In [23]:
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fig,ax = plt.subplots(figsize=(10, 8))
sns.heatmap(pd_nor_mat, cmap="Blues")
plt.ylabel('scsa_celltype', fontsize=30,fontweight='bold')
plt.xlabel('manual_celltype', fontsize=30,fontweight='bold')
plt.xticks(fontsize=24,fontweight='bold')
plt.yticks(fontsize=24,fontweight='bold')
plt.savefig('confusion.png', dpi=300,bbox_inches = 'tight',transparent=True)
fig,ax = plt.subplots(figsize=(10, 8))
sns.heatmap(pd_nor_mat, cmap="Blues")
plt.ylabel('scsa_celltype', fontsize=30,fontweight='bold')
plt.xlabel('manual_celltype', fontsize=30,fontweight='bold')
plt.xticks(fontsize=24,fontweight='bold')
plt.yticks(fontsize=24,fontweight='bold')
plt.savefig('confusion.png', dpi=300,bbox_inches = 'tight',transparent=True)
In [ ]:
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results_file = 'crc2.h5ad'
adata.write(results_file)
results_file = 'crc2.h5ad'
adata.write(results_file)